Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Scene text detection using sparse stroke information and MLP

Abstract: In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region Estimation of the uniformity in stroke thickness on the basis of sparse sampling of the distance transform values is a novel approach Another feature is the distance between the foreground and background colors computed in a perceptually uniform and illumination-invariant color space Remaining features include two ratios of anti-parallel edge gradient orientations, a regularity measure between the skeletal representation and Canny edgemap of the object, average edge gradient magnitude, variation in the foreground gray levels and five others Here, we present the results of the proposed approach on the ICDAR 2003 database and another database of scene images consisting of text of Indian scripts

Fast Computing Adaptively Sampled Distance Field on GPU

Abstract: In this paper we present an efficient method to compute the signed distance field for a large triangle mesh, which can run interactively with GPU accelerated. Restricted by absence of flexible pointer addressing on GPU, we design a novel multi-layer hash table to organize the voxel/triangle overlap pairs as two-tuples, such strategy provides an efficient way to store and access. Based on the general octree structure idea, a GPU-based octree structure is given to generate the sample points which are used to calculate the shortest distance to the triangle mesh. Classifying sample points into three types provides a well tradeoff between performance and precision, and when implementing the algorithm on GPU, these samples are also organized into blocks to share the triangles among threads to save bandwidth. Finally we demonstrate efficient calculation of the global signed distance field for some typical large triangle meshes with pseudo-normal method. Compared to previous work, our algorithm is quite fast in performance.

Fast multi-aspect 2D human detection

Abstract: We address the problem of detecting human figures in images, taking into account that the image of the human figure may be taken from a range of viewpoints. We capture the geometric deformations of the 2D human figure using an extension of the Common Factor Model (CFM) of Lan and Huttenlocher. The key contribution of the paper is an improved iterative message passing inference algorithm that runs faster than the original CFM algorithm. This is based on the insight that messages created using the distance transform are shift invariant and therefore messages can be created once and then shifted for subsequent iterations. Since shifting (O(1) complexity) is faster than computing a distance transform (O(n) complexity), a significant speedup is observed in the experiments. We demonstrate the effectiveness of the new model for the human parsing problem using the Iterative Parsing data set and results are competitive with the state of the art detection algorithm of Andriluka, et al.

Texture-adaptive image colorization framework

Abstract: In this paper we present how to exploit the textural information to improve scribble-based image colorization. Although many methods have been already proposed for coloring grayscale images based on a set of color scribbles inserted by a user, very few of them take into account textural properties. We demonstrate that the textural information can be extremely helpful for this purpose and it may greatly simplify the colorization process. First, based on a scribbled image we determine the most discriminative textural features using linear discriminant analysis. This makes it possible to boost the initial scribbles by adjoining the regions having similar textural properties. After that, we determine the color propagation paths and compute chrominance of every pixel in the image. For the propagation process we used two competing path cost metrics which are dynamically selected for every scribble. Using these metrics it is possible to efficiently propagate chrominance both over smooth and rough image regions. Texture-based scribble boosting followed by competitive color propagation is the main contribution of the work reported here. Extensive experimental validation documented in this paper demonstrates that image colorization can be substantially improved using the proposed technique.